Pretest-Posttest Measure of Introductory Computer Students\u27 Attitudes toward Computers

An exploratory study was conducted in multiple sections of an introductory computer course to determine whether an introductory computer course changed computer attitudes. A sample of 329 individuals were given a computer attitude measurement (ATCUS) the first and last day of an introductory computer class. We have strong evidence to conclude that those enrolled in the class had worse attitudes after the class than before

A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification

In this paper we focus on cell phenotype image classification, a bioimaging problem that is concerned with finding the location of protein expressions within a cell. Protein localization is becoming increasingly critical in the diagnosis and prognosis of many diseases. In recent years several new approaches for describing a given image have been proposed. Some of the most significant developments have been based on binary encodings, such as local binary patterns and local phase quantization. In this paper we reexamine one of the oldest methods for representing an image that Haralick famously proposed in 1979 using the co-occurrence matrix for calculating a set of image statistics. Few methods have been proposed since that extract new features from the co-occurrence matrix. In this work we compare some recently proposed methods that are based on the co-occurrence matrix (CM) to classify cell phenotype images. We investigate the correlation among the different sets of features that can be extracted from the CM and then determine the best way to combine these different feature sets for optimizing system performance. Moreover, we combine our novel approach with state of the art descriptors to optimize performance. We validate our approach on various types of biological microscope images using five image databases for subcellular classification. We use these image features for training a stand-alone support vector machine and a random subspace of support vector machines to separate the classes in each dataset

Different approaches for extracting information from the co-occurrence matrix

In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione = 3314&IDgruppo_pass = 124&preview=

Different approaches for extracting information from the co-occurrence matrix

In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test

A Gender-Based Categorization for Conflict Resolution

Purpose: As the workforce becomes increasingly diversified, it becomes increasingly important for managers to understand the conflict resolution attitudes brought to information systems (IS) by both men and women. This research was designed to investigate assumptions that may exist regarding the relationship between gender and conflict resolution. Specifically, the intent of this study was to compare the conflict resolution strategies of males and females majoring in IS in order to determine if gender‐based differences exist. Design/methodology/approach: The Thomas‐Kilmann Conflict Mode Instrument was utilized to assess the conflict resolution styles of 163 traditional‐age (18‐22) students enrolled in undergraduate IS courses at a large Midwestern university. Both ANOVA and t‐test analyses were utilized to investigate the relationship between gender and conflict resolution style. Findings: Results of this study indicate that, when compared with their male counterparts, women are more likely to utilize a collaborative conflict resolution style and men are more likely to avoid conflict. As collaboration is generally considered more productive and avoidance more disruptive in the conflict resolution process, the study suggests that women may possess more effective conflict resolution attributes than their male counterparts. Originality/value: The results of this paper lend support to the theory that an individual\u27s gender may be related to the development of conflict resolution styles. These findings also support the premise that female students in IS are highly adapted with regard to their ability to work collaboratively (and thereby successfully) in situations where conflict is likely to occur